Mass spectrometric analysis of 7‐oxygenated androst‐5‐ene structures. Influence in trimethylsilyl derivative formation
Bibliographic record
Abstract
Several authors have described the generation of androsta-3,5-diene-7-one structures from androst-5-ene-3,7-dione or androst-5-ene-3β-ol-7-one under acidic conditions and/or at high temperatures. The goal of this study was to observe and to describe the results obtained after the chromatographic analysis of the trimethylsilyl derivatives of reference materials of 7-oxo-DHEA, 7α-hydroxy-DHEA, 7β-hydroxy-DHEA, and androsta-3,5-diene-7,17-dione known as arimistane. METHODS: The purity of the analyte reference materials was verified by liquid chromatography/quadrupole mass spectrometry. The trimethylsilyl derivatives obtained using several mixtures with MSTFA (N-methyl-N-trimethylsilyl trifluoroacetamide) in comparison with solely MSTFA were analyzed by gas chromatography coupled to a time-of-flight detector equipped with a multimode inlet or to a simple quadrupole detector with a split/splitless inlet. RESULTS: The study showed that the formation of arimistane from 7-oxo-DHEA occurs using common derivatization reagents used for the analyses by gas chromatography (GC). In addition, the formation of the enolized TMS derivative of 7-oxo-DHEA was observed in considerable amount when it was reacted with MSTFA. The analysis of 7α-hydroxy-DHEA resulted in the detection of ~1% of arimistane. The formation of unexpected artifacts from derivatization is influenced by the reagent itself, the reaction temperature, the inlet used and its configuration. CONCLUSIONS: The derivatization reagent, instrumental conditions (inlet), as well as the chemical structures of the analytes present in the matrix, can influence the results. So, before describing a new feature as a potential "new" metabolite, special caution must be taken since we could actually be dealing with an artifact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".